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Detecting spikes of wheat plants using neural networks with Laws texture energy

Overview of attention for article published in Plant Methods, October 2017
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Title
Detecting spikes of wheat plants using neural networks with Laws texture energy
Published in
Plant Methods, October 2017
DOI 10.1186/s13007-017-0231-1
Pubmed ID
Authors

Li Qiongyan, Jinhai Cai, Bettina Berger, Mamoru Okamoto, Stanley J. Miklavcic

Abstract

The spike of a cereal plant is the grain-bearing organ whose physical characteristics are proxy measures of grain yield. The ability to detect and characterise spikes from 2D images of cereal plants, such as wheat, therefore provides vital information on tiller number and yield potential. We have developed a novel spike detection method for wheat plants involving, firstly, an improved colour index method for plant segmentation and, secondly, a neural network-based method using Laws texture energy for spike detection. The spike detection step was further improved by removing noise using an area and height threshold. The evaluation results showed an accuracy of over 80% in identification of spikes. In the proposed method we also measure the area of individual spikes as well as all spikes of individual plants under different experimental conditions. The correlation between the final average grain yield and spike area is also discussed in this paper. Our highly accurate yield trait phenotyping method for spike number counting and spike area estimation, is useful and reliable not only for grain yield estimation but also for detecting and quantifying subtle phenotypic variations arising from genetic or environmental differences.

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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 75 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 16%
Researcher 11 15%
Student > Master 11 15%
Student > Bachelor 5 7%
Student > Doctoral Student 3 4%
Other 7 9%
Unknown 26 35%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 27%
Computer Science 12 16%
Engineering 8 11%
Earth and Planetary Sciences 3 4%
Psychology 1 1%
Other 3 4%
Unknown 28 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 October 2017.
All research outputs
#14,956,881
of 23,005,189 outputs
Outputs from Plant Methods
#781
of 1,088 outputs
Outputs of similar age
#192,704
of 325,897 outputs
Outputs of similar age from Plant Methods
#27
of 34 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,088 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 325,897 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.